MIT School of Management
Recent publications
Impact Statement This research provides critical insights into the relationship between crude oil price volatility (COPV) and economic growth (EG) within OECD countries, offering significant contributions to macroeconomic theory and policy formulation. By employing robust panel data estimation techniques, including dynamic panel data analysis and system GMM, the study examines the adverse effects of COPV on the economic performance of both oil-exporting and oil-importing nations over a 23-year period. The findings are particularly relevant for policymakers in oil-dependent economies, where economic stability is more vulnerable to oil price fluctuations. The research challenges traditional economic models by highlighting the asymmetric and nuanced nature of oil price effects, emphasizing the need for tailored risk management strategies and macroeconomic policies to mitigate the unpredictable impacts of COPV on growth. The theoretical advancements made in understanding the complex interplay between oil prices, COPV, and economic growth extends the existing knowledge in macroeconomics, international trade, and econometrics. These insights can be leveraged to enhance economic resilience in the face of volatile global oil markets.
In this paper, we target the problem of generating facial expressions from a piece of audio. This is challenging since both audio and video have inherent characteristics that are distinct from the other. Some words may have identical lip movements, and speech impediments may prevent lip-reading in some individuals. Previous approaches to generating such a talking head suffered from stiff expressions. This is because they focused only on lip movements and the facial landmarks did not contain the information flow from the audio. Hence, in this work, we employ spatio-temporal independent component analysis to accurately sync the audio with the corresponding face video. Proper word formation also requires control over the face muscles that can be captured using a barrier function. We first validated the approach on the diffusion of salt water in coastal areas using a synthetic finite element simulation. Next, we applied it to 3D facial expressions in toddlers for which training data is difficult to capture. Prior knowledge in the form of rules is specified using Fuzzy logic, and multi-objective optimization is used to collectively learn a set of rules. We observed significantly higher F-measure on three real-world problems.
The panel brings together scholars of technology, innovation, and organization to discuss the emergent research agenda on the design processes behind technologies of coordination and control, such as digital platforms, big data analytics, and AI. Following recent calls by Bailey, Barley, Orlikowski, and others, five panelists will discuss why a focus on agendas, ideologies, and power-relations of designers as well as the organizational structure of design processes is critical to understand the impact of these technologies on organizations, markets, and work more generally. Drawing on empirical case studies of technology design, the panelists will discuss on how to think about the interplay between design and use of coordination and control technologies, how to solve data and access problems, and what are the salient theoretical issues for research on technology, work, and organizations.
Just as the telescope ushered in a new era of discovery by broadening astronomers’ sense of sight, digital technology has made it possible to examine human behavior with newfound precision and granularity. Studies of teams now take place in scalable virtual laboratories (Almaatouq et al., 2021); virtual avatars can replace human confederates (de Melo et al. 2013; Krafft et al., 2017; Grossmann et al., 2023); and turn-by-turn analysis of facial and vocal cues trace the underpinnings of team effectiveness to its subtlest unspoken behaviors (Hung and Gatica-Perez, 2010; Reece et al., 2023). Innovations in instrumentation have long fueled innovations in experimentation, and recent technological advances have been no exception. However, unlike previous advances in scientific instrumentation, digital technology is more than a tool for measurement. Telescopes merely reveal the form of stars that have existed for millennia; they do not change their movements or their nature. Digital technology, however, has profoundly transformed the nature of teamwork (Larson and DeChurch, 2020). Indeed, even the theme of this year’s conference — Innovating for the Future — draws attention to how “dramatic technological shifts” impact future organizations. Thus, as much as digital technology has produced new data and methods to examine longstanding questions about teamwork, it has also, in parallel, transformed what it means to work in a team to begin with. This symposium weaves together technology’s dual implications on teams and on the science of teamwork. Across four original research papers, we will demonstrate that studying teams in a digital setting is more than simply a recreation of in-person interactions, but is rather a rich setting for methodological innovation. These innovations, in turn, push the boundaries of our knowledge about teamwork, particularly in a world of increasingly technology-mediated collaboration. We call this bidirectional interplay between methods and theory a computational science of collaboration. Through conversation with Laurie Weingart and Randall Peterson, both esteemed scholars in organizational behavior and conflict management, we will investigate how this computational science will influence our field’s research agenda in the years to come. This core premise aligns with both theoretical and methodological trends in management. Scholarship on teamwork has increasingly shifted from studying teams with simple inputs and mediators towards viewing teams as dynamic and multimodal (Humphrey and Aime, 2014; Mathieu et al., 2018). In conflict management, the conceptualization of conflict as discrete categories — e.g., task, relationship, and process — has given way to a multidimensional perspective that acknowledges distinct characteristics of conflict expression (Weingart et al., 2015). Complementing the increased richness in theories, computational advances have produced richer data and modes of analysis. Besides spoken words and written text, there are prosodic features (e.g., pitch, loudness), the cadence of speaking turns, facial expressions, hand gestures, and body language (Zeng et al., 2007; Ranganath et al., 2013; Reece et al., 2023). These new methods and behaviors, in turn, motivate fresh theorizing about long-standing problems in studies of teams and groups, conflict, and decision-making. Our symposium seeks to highlight this virtuous cycle and its implications for both management scholars and practitioners. Each of our four presentations blends methodological innovation with practical implications, addressing two broad themes: first, how do new tools and technologies provide new kinds of data, research designs, and modes of analysis? Second, how might this computational science of collaboration produce fresh theories? For practitioners, our symposium offers insights about conversational strategies, task design, and medium choice on the ability to work together effectively in a digitized world; for researchers, we present new tools to scalably analyze rich, multimodal data and to advance a dynamic understanding of teamwork.
Digital algorithms try to display content that engages consumers. To do this, algorithms need to overcome a ‘cold-start problem’ by swiftly learning whether content engages users. This requires feedback from users. The algorithm targets segments of users. However, if there are fewer individuals in a targeted segment of users, simply because this group is rarer in the population, this could lead to uneven outcomes for minority relative to majority groups. This is because individuals in a minority segment are proportionately more likely to be test subjects for experimental content that may ultimately be rejected by the platform. We explore in the context of ads that are displayed following searches on Google whether this is indeed the case. Previous research has documented that searches for names associated in a US context with Black people on search engines were more likely to return ads that highlighted the need for a criminal background check than was the case for searches for white people. We implement search advertising campaigns that target ads to searches for Black and white names. Our ads are indeed more likely to be displayed following a search for a Black name, even though the likelihood of clicking was similar. Since Black names are less common, the algorithm learns about the quality of the underlying ad more slowly. As a result, an ad is more likely to persist for searches next to Black names than next to white names. Proportionally more Black name searches are likely to have a low-quality ad shown next to them, even though eventually the ad will be rejected. A second study where ads are placed following searches for terms related to religious discrimination confirms this empirical pattern. Our results suggest that as a practical matter, real-time algorithmic learning can lead minority segments to be more likely to see content that will ultimately be rejected by the algorithm.
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26 members
Utkarsh Chourange
  • Department of Computer Application & Management
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